Note
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Split Dataset Example#
In this example, we aim to show multiple ways of how you can split your datasets for training, testing, and evaluating your models.
# Authors: Lukas Gemein <l.gemein@gmail.com>
#
# License: BSD (3-clause)
from braindecode.datasets import MOABBDataset
from braindecode.preprocessing import create_windows_from_events
Loading the dataset#
Firstly, we create a dataset using the braindecode
MOABBDataset to load
it fetched from MOABB. In this example, we’re using Dataset 2a from BCI
Competition IV.
dataset = MOABBDataset(dataset_name="BNCI2014001", subject_ids=[1])
BNCI2014001 has been renamed to BNCI2014_001. BNCI2014001 will be removed in version 1.1.
The dataset class name 'BNCI2014001' must be an abbreviation of its code 'BNCI2014-001'. See moabb.datasets.base.is_abbrev for more information.
Splitting#
By description information#
The class MOABBDataset has a pandas
DataFrame containing additional description of its internal datasets,
which can be used to help splitting the data
based on recording information, such as subject, session, and run of each trial.
dataset.description
Here, we’re splitting the data based on different runs. The method split returns a dictionary with string keys corresponding to unique entries in the description DataFrame column.
splits = dataset.split("run")
print(splits)
splits["4"].description
{'0': <braindecode.datasets.base.BaseConcatDataset object at 0x7fbc86e65250>, '1': <braindecode.datasets.base.BaseConcatDataset object at 0x7fbc5b799f70>, '2': <braindecode.datasets.base.BaseConcatDataset object at 0x7fbc5d539b80>, '3': <braindecode.datasets.base.BaseConcatDataset object at 0x7fbc5d20a840>, '4': <braindecode.datasets.base.BaseConcatDataset object at 0x7fbc863c2330>, '5': <braindecode.datasets.base.BaseConcatDataset object at 0x7fbc86bef560>}
By row index#
Another way we can split the dataset is based on a list of integers corresponding to rows in the description. In this case, the returned dictionary will have ‘0’ as the only key.
splits = dataset.split([0, 1, 5])
print(splits)
splits["0"].description
{'0': <braindecode.datasets.base.BaseConcatDataset object at 0x7fbc84155a00>}
However, if we want multiple splits based on indices, we can also define a list containing lists of integers. In this case, the dictionary will have string keys representing the index of the dataset split in the order of the given list of integers.
splits = dataset.split([[0, 1, 5], [2, 3, 4], [6, 7, 8, 9, 10, 11]])
print(splits)
splits["2"].description
{'0': <braindecode.datasets.base.BaseConcatDataset object at 0x7fbc58267f50>, '1': <braindecode.datasets.base.BaseConcatDataset object at 0x7fbc58264410>, '2': <braindecode.datasets.base.BaseConcatDataset object at 0x7fbc58267b00>}
You can also name each split in the output dictionary by specifying the keys of each list of indexes in the input dictionary:
splits = dataset.split(
{"train": [0, 1, 5], "valid": [2, 3, 4], "test": [6, 7, 8, 9, 10, 11]}
)
print(splits)
splits["test"].description
{'train': <braindecode.datasets.base.BaseConcatDataset object at 0x7fbc5d539b80>, 'valid': <braindecode.datasets.base.BaseConcatDataset object at 0x7fbc582669f0>, 'test': <braindecode.datasets.base.BaseConcatDataset object at 0x7fbc582646b0>}
Observation#
Similarly, we can split datasets after creating windows using the same methods.
windows = create_windows_from_events(
dataset, trial_start_offset_samples=0, trial_stop_offset_samples=0
)
Used Annotations descriptions: ['feet', 'left_hand', 'right_hand', 'tongue']
Used Annotations descriptions: ['feet', 'left_hand', 'right_hand', 'tongue']
Used Annotations descriptions: ['feet', 'left_hand', 'right_hand', 'tongue']
Used Annotations descriptions: ['feet', 'left_hand', 'right_hand', 'tongue']
Used Annotations descriptions: ['feet', 'left_hand', 'right_hand', 'tongue']
Used Annotations descriptions: ['feet', 'left_hand', 'right_hand', 'tongue']
Used Annotations descriptions: ['feet', 'left_hand', 'right_hand', 'tongue']
Used Annotations descriptions: ['feet', 'left_hand', 'right_hand', 'tongue']
Used Annotations descriptions: ['feet', 'left_hand', 'right_hand', 'tongue']
Used Annotations descriptions: ['feet', 'left_hand', 'right_hand', 'tongue']
Used Annotations descriptions: ['feet', 'left_hand', 'right_hand', 'tongue']
Used Annotations descriptions: ['feet', 'left_hand', 'right_hand', 'tongue']
# Splitting by different runs
print("Using description info")
splits = windows.split("run")
print(splits)
print()
# Splitting by row index
print("Splitting by row index")
splits = windows.split([4, 8])
print(splits)
print()
print("Multiple row index split")
splits = windows.split([[4, 8], [5, 9, 11]])
print(splits)
print()
# Specifying output's keys
print("Specifying keys")
splits = windows.split(dict(train=[4, 8], test=[5, 9, 11]))
print(splits)
Using description info
{'0': <braindecode.datasets.base.BaseConcatDataset object at 0x7fbc5c819250>, '1': <braindecode.datasets.base.BaseConcatDataset object at 0x7fbc669ab4d0>, '2': <braindecode.datasets.base.BaseConcatDataset object at 0x7fbc863c1c70>, '3': <braindecode.datasets.base.BaseConcatDataset object at 0x7fbc58266ff0>, '4': <braindecode.datasets.base.BaseConcatDataset object at 0x7fbc58267260>, '5': <braindecode.datasets.base.BaseConcatDataset object at 0x7fbc58265010>}
Splitting by row index
{'0': <braindecode.datasets.base.BaseConcatDataset object at 0x7fbc582646b0>}
Multiple row index split
{'0': <braindecode.datasets.base.BaseConcatDataset object at 0x7fbc669ab4d0>, '1': <braindecode.datasets.base.BaseConcatDataset object at 0x7fbc841559d0>}
Specifying keys
{'train': <braindecode.datasets.base.BaseConcatDataset object at 0x7fbc5d539b80>, 'test': <braindecode.datasets.base.BaseConcatDataset object at 0x7fbc58264170>}
Total running time of the script: (0 minutes 6.476 seconds)
Estimated memory usage: 873 MB